MATLAB Code Implementation for Image Binarization with Adaptive Thresholding

This program enables automated image binarization in MATLAB with intelligent threshold selection capabilities. The implementation uses histogram analysis and statistical methods to determine optimal threshold values dynamically, eliminating manual parameter tuning. Key functions include grayscale conversion, threshold calculation using Otsu's algorithm (which maximizes inter-class variance), and pixel-wise classification into foreground/background. The code structure involves loading images through imread(), converting to grayscale via rgb2gray() if needed, computing thresholds with graythresh(), and applying binary transformation using imbinarize(). This solution provides an efficient pipeline for converting color or grayscale images to binary format while maintaining edge details and object contours. The algorithm automatically adapts to varying lighting conditions and image characteristics, making it suitable for document processing, medical imaging, and computer vision applications. Users can quickly achieve desired binarization results through this optimized implementation that combines MATLAB's image processing toolbox functions with custom threshold optimization logic.